为什么 numpy 数组比列表花费更多时间？

Question

Why is numpy slower in this code?

for i in range(10000):
    array = [[0.0,] * 1024 for x in range(1024)]

0,021539204 seconds time elapsed (39.616.810 instructions)

import numpy as np
for i in range(10000):
    array = np.zeros((1024,1024))

0.209111860 seconds time elapsed (1.067.923.180 instructions)

Answer 1

Are you running in the exact same machine? I'm getting faster result in numpy .

In [7]: %%time
   ...: import numpy as np
   ...: for i in range(10000):
   ...:     array = np.zeros((1024,1024))
   ...:
CPU times: user 3.33 s, sys: 0 ns, total: 3.33 s
Wall time: 3.32 s

In [8]: %%time
   ...: for i in range(10000):
   ...:     array = [[0.0,] * 1024 for x in range(1024)]
   ...:
CPU times: user 1min 14s, sys: 0 ns, total: 1min 14s
Wall time: 1min 14s

This answer in the numpy vs list thread also agrees.